In [ ]:
from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import math
import requests
import json
import re
import csv
directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL_test.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\rd\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\rd\\final_road_output_1')
# Read the CSV file
data = pd.read_csv(csv_path)
# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
numeric_part = ''.join(filter(str.isdigit, filename))
return int(numeric_part) if numeric_part else None
def create_binary_mask(arr, target_color, threshold=30):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def extract_building_regions(arr, target_color, threshold=10):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
# def find_max_building_storeys(gpr):
# max_building_storeys= 0
# if gpr >= 0 and gpr < 1.4:
# max_building_storeys = 5
# elif gpr >= 1.4 and gpr < 1.6:
# max_building_storeys = 12
# elif gpr >= 1.6 and gpr < 2.1:
# max_building_storeys = 24
# elif gpr >= 2.1 and gpr < 2.8:
# max_building_storeys = 36
# elif gpr >= 2.8:
# max_building_storeys = 48 ## by right got no limit
# return max_building_storeys
def masked_rgb(simp_gpr):
rgb = [0,0,0]
if simp_gpr == 1.4:
rgb = [0,255,0]
elif simp_gpr == 1.6:
rgb = [200,130,60]
elif simp_gpr == 2.1:
rgb = [0,0,0]
elif simp_gpr == 2.8:
rgb = [255,0,0]
elif simp_gpr == 3.0:
rgb =[0,0,255]
return rgb
'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''
# absolute_accuracies = []
# losses =[]
# images =[]
# sanity_ratios =[]
gprs =[]
generated_gprs =[]
sanity_ratios =[]
# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
if image_file.endswith('.png'):
image_index = extract_numeric_part(image_file)
# Construct the path for the corresponding masked image
gt_mask_image_filename = f"{image_index}.png"
gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
open_gt_mask_image = Image.open(gt_mask_image)
mask_crop_box = (512, 0, 1024, 512) # right side
mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
gt_crop_box = (0, 0, 512, 512) # left side
gt_image = open_gt_mask_image.crop(gt_crop_box)
generated_image = os.path.join(generated_image_path, image_file)
generated_image = Image.open(generated_image)
# Check if the image index matches any index in the CSV
matched_row = data[data['key1'] == image_index]
if not matched_row.empty:
# Extract the GPR value for the matched row
gpr_value = matched_row['GPR'].iloc[0]
storey = matched_row['storeys'].iloc[0]
simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
actual_site_area = matched_row['area'].iloc[0]
actual_site_area = actual_site_area.replace(',', '')
actual_site_area = float(actual_site_area[:-4])
gpr_value = float(gpr_value)
storey = int(storey)
mask_array = np.array(mask_image)
generated_array = np.array(generated_image)
mask_color = masked_rgb(simplified_gpr_value)
site_mask = create_binary_mask(mask_array, mask_color)
site_area_array = generated_array.copy()
site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
site_area_image = Image.fromarray(site_area_array)
mask_color = [255, 10, 169] # pink
building_mask = extract_building_regions(site_area_array, mask_color)
buildings_image = Image.fromarray(building_mask)
plt.figure(figsize=(20, 5))
plt.subplot(1, 4, 1)
plt.imshow(mask_image)
plt.title('Mask Image')
plt.axis('off')
plt.subplot(1, 4, 2)
plt.imshow(gt_image)
plt.title('GT Image')
plt.axis('off')
plt.subplot(1, 4, 3)
plt.imshow(generated_image)
plt.title('Generated Image')
plt.axis('off')
plt.subplot(1, 4, 4)
plt.imshow(buildings_image, cmap='gray')
plt.title('Buildings Image')
plt.axis('off')
plt.show()
# accuracy
building_pixels = np.sum(building_mask)
mask_pixels = np.sum(site_mask)
msq_per_pixel = actual_site_area/mask_pixels
building_area = msq_per_pixel * building_pixels
#max_storeys = find_max_building_storeys(gpr_value)
generated_gpr = building_area*storey/actual_site_area
gprs.append(gpr_value)
generated_gprs.append(generated_gpr)
# if generated_gpr == 0:
# accuracy = 0
# else:
# accuracy = (gpr_value - generated_gpr) / gpr_value #gpr_value is the target gpr
# loss =
# images.append(image_file)
# absolute_accuracy = abs(accuracy)
# absolute_accuracies.append(absolute_accuracy)
print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Storeys:{storey}, Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')
#sanity check. ratios should be about 0.75
ratio = mask_pixels/actual_site_area
sanity_ratios.append(ratio)
total_data = len(gprs)
accuracies = []
absolute_error =[]
square_error =[]
for tar_gpr, gen_gpr in zip(gprs, generated_gprs):
accuracies.append(abs((tar_gpr-gen_gpr)/tar_gpr))
absolute_error.append(abs(tar_gpr-gen_gpr))
square_error.append((tar_gpr-gen_gpr)**2)
accuracy = sum(accuracies)/total_data
mean_abs_error = sum(absolute_error)/total_data
root_squared_error = math.sqrt(sum(square_error)/total_data)
print(f"Accuracies:{accuracies} \nSquare error:{square_error} \nAbsolute error:{absolute_error} ")
print(f"\nAccuracy:{accuracy} MAE:{mean_abs_error} RMSE:{root_squared_error}")
Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 23065.1, Building pixels: 4990, Mask pixels: 16203, Generated GPR: 1.5398383015490957
Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Storeys:12, Site area: 37265.0, Building pixels: 3559, Mask pixels: 27286, Generated GPR: 1.5651982701751814
Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:36, Site area: 10414.2, Building pixels: 1021, Mask pixels: 8468, Generated GPR: 4.340576287198867
Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Storeys:12, Site area: 6157.3, Building pixels: 1631, Mask pixels: 4749, Generated GPR: 4.121288692356286
Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:15, Site area: 19547.0, Building pixels: 2102, Mask pixels: 14230, Generated GPR: 2.2157413914265636
Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 17455.9, Building pixels: 4610, Mask pixels: 12216, Generated GPR: 1.8868696791093647
Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:15, Site area: 22094.4, Building pixels: 2196, Mask pixels: 16171, Generated GPR: 2.0369797786160415
Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 10097.1, Building pixels: 1244, Mask pixels: 7571, Generated GPR: 2.793290186236957
Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13564.8, Building pixels: 1756, Mask pixels: 9875, Generated GPR: 3.022987341772151
Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:18, Site area: 27418.2, Building pixels: 3175, Mask pixels: 21711, Generated GPR: 2.6323062042282714
Image: 1670.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:13, Site area: 17940.2, Building pixels: 1520, Mask pixels: 11713, Generated GPR: 1.6870144284128745
Image: 1796.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:17, Site area: 13877.2, Building pixels: 1192, Mask pixels: 9260, Generated GPR: 2.1883369330453566
Image: 1811.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 7255.7, Building pixels: 2095, Mask pixels: 5237, Generated GPR: 2.0001909490166128
Image: 1876.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:19, Site area: 10502.8, Building pixels: 1474, Mask pixels: 8220, Generated GPR: 3.4070559610705593
Image: 191.png, GPR: 3.5, Simplified GPR: 3.0, Storeys:18, Site area: 13000.3, Building pixels: 2180, Mask pixels: 9133, Generated GPR: 4.29650717179459
Image: 2000.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13241.8, Building pixels: 2103, Mask pixels: 9581, Generated GPR: 3.7314476568207913
Image: 434.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:16, Site area: 39401.6, Building pixels: 3629, Mask pixels: 28557, Generated GPR: 2.0332667997338656
Image: 489.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:15, Site area: 28692.65, Building pixels: 2616, Mask pixels: 20400, Generated GPR: 1.923529411764706
Image: 491.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:16, Site area: 18747.8, Building pixels: 1892, Mask pixels: 12952, Generated GPR: 2.3372452130945027
Image: 568.png, GPR: 3.4, Simplified GPR: 3.0, Storeys:19, Site area: 14344.0, Building pixels: 1877, Mask pixels: 10424, Generated GPR: 3.42123944742901 Accuracies:[0.09988450110649702, 0.37392069192992744, 0.5502058168567383, 1.5758054327226785, 0.2614195361911455, 0.3477640565066891, 0.27250722192284227, 0.06890327125434774, 0.007662447257383705, 0.2534791448706054, 0.39749484699540194, 0.21845109534094403, 0.42870782072615204, 0.62240760050979, 0.22757347765559718, 0.24381588560693043, 0.03177771441244499, 0.08403361344537817, 0.22091826230183242, 0.0062468963026500865] Square error:[0.019554750580135855, 0.873854274083473, 2.373375296679446, 6.356896670203669, 0.6150615651215425, 0.2370420844360558, 0.5821998582408248, 0.04272894710595201, 0.0005284178817496836, 0.28334989505991015, 1.23873688256112, 0.3741317074763604, 0.36022917528146237, 1.7083952853700832, 0.6344236747202168, 0.535015674668626, 0.004453320017760013, 0.03114186851211074, 0.4392439075661511, 0.000451114127089692] Absolute error:[0.13983830154909582, 0.9348017298248186, 1.540576287198867, 2.521288692356286, 0.7842586085734364, 0.48686967910936474, 0.7630202213839583, 0.2067098137630432, 0.022987341772151115, 0.5323062042282714, 1.1129855715871253, 0.6116630669546432, 0.6001909490166129, 1.3070559610705592, 0.7965071717945902, 0.7314476568207913, 0.0667332002661345, 0.17647058823529416, 0.6627547869054973, 0.021239447429010294] Accuracy:0.3146489666957988 MAE:0.7009852639919776 RMSE:0.9140791642328836